class_docstr: A class that contains all the information needed by ONNX Runtime optimization for a given model type.
  
  Attributes:
      _conf (`Dict[str]`):
          A dictionary mapping each supported model type to the corresponding ONNX Runtime model type.
code: def check_optimization_supported_model(
      cls, model_type: str, optimization_config
  ):
      # as of 1.15.O: https://github.com/microsoft/onnxruntime/blob/v1.15.0/onnxruntime/python/tools/transformers/optimizer.py#L42
      supported_model_types_for_optimization = [
          "bart",
          "bert",
          "gpt2",
          "tnlr",
          "t5",
          "unet",
          "vae",
          "clip",
          "vit",
          "swin",
      ]
      model_type = model_type.replace("_", "-")
      if (model_type not in cls._conf) or (
          cls._conf[model_type]
          not in supported_model_types_for_optimization
      ):
          raise NotImplementedError(
              f"ONNX Runtime doesn't support the graph optimization of {model_type} yet. Only {list(cls._conf.keys())} are supported. "
              f"If you want to support {model_type} please propose a PR or open up an issue in ONNX Runtime: https://github.com/microsoft/onnxruntime."
          )
  
imports: import os
  import re
  from enum import Enum
  from inspect import signature
  from typing import Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  from packaging import version
  from transformers.utils import logging
  import onnxruntime
  from ..exporters.onnx import OnnxConfig, OnnxConfigWithLoss
  from ..utils.import_utils import _is_package_available
label_desc: Use image node_id 3 for calling the ORTConfigManager obj's underlying member method code with example usage: obj.check_optimization_supported_model(cls, model_type, optimization_config) without return types